Correction of catalogue and version setting

[modules/adao.git] / doc / en / ref_algorithm_3DVAR.rst

diff --git a/doc/en/ref_algorithm_3DVAR.rst b/doc/en/ref_algorithm_3DVAR.rst
index 22ae3b490d76ad08ff57ceac946d304337d85e24..a946bbb973521baa083cec091cde0188646c8be2 100644 (file)
--- a/doc/en/ref_algorithm_3DVAR.rst
+++ b/doc/en/ref_algorithm_3DVAR.rst
@@ -41,6 +41,7 @@ which is usually designed as the "*3D-VAR*" function (see for example
 Optional and required commands
 ++++++++++++++++++++++++++++++
 
+.. index:: single: AlgorithmParameters
 .. index:: single: Background
 .. index:: single: BackgroundError
 .. index:: single: Observation
@@ -52,7 +53,6 @@ Optional and required commands
 .. index:: single: CostDecrementTolerance
 .. index:: single: ProjectedGradientTolerance
 .. index:: single: GradientNormTolerance
-.. index:: single: StoreInternalVariables
 .. index:: single: StoreSupplementaryCalculations
 .. index:: single: Quantiles
 .. index:: single: SetSeed
@@ -96,10 +96,11 @@ following:
     :math:`(X,U)`.
 
 The general optional commands, available in the editing user interface, are
-indicated in :ref:`section_ref_assimilation_keywords`. In particular, the
-optional command "*AlgorithmParameters*" allows to choose the specific options,
+indicated in :ref:`section_ref_assimilation_keywords`. Moreover, the parameters
+of the command "*AlgorithmParameters*" allows to choose the specific options,
 described hereafter, of the algorithm. See
-:ref:`section_ref_options_AlgorithmParameters` for the good use of this command.
+:ref:`section_ref_options_Algorithm_Parameters` for the good use of this
+command.
 
 The options of the algorithm are the following:
 
@@ -157,21 +158,17 @@ The options of the algorithm are the following:
 
     Example : ``{"GradientNormTolerance":1.e-5}``
 
-  StoreInternalVariables
-    This Boolean key allows to store default internal variables, mainly the
-    current state during iterative optimization process. Be careful, this can be
-    a numerically costly choice in certain calculation cases. The default is
-    "False".
-
-    Example : ``{"StoreInternalVariables":True}``
-
   StoreSupplementaryCalculations
     This list indicates the names of the supplementary variables that can be
     available at the end of the algorithm. It involves potentially costly
     calculations or memory consumptions. The default is a void list, none of
     these variables being calculated and stored by default. The possible names
-    are in the following list: ["APosterioriCovariance", "BMA", "OMA", "OMB",
-    "Innovation", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles"].
+    are in the following list: ["APosterioriCorrelations",
+    "APosterioriCovariance", "APosterioriStandardDeviations",
+    "APosterioriVariances", "BMA", "CostFunctionJ", "CurrentState", "OMA",
+    "OMB", "Innovation", "SigmaObs2", "MahalanobisConsistency",
+    "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState",
+    "SimulatedObservationAtOptimum", "SimulationQuantiles"].
 
     Example : ``{"StoreSupplementaryCalculations":["BMA","Innovation"]}``
 
@@ -251,12 +248,30 @@ The unconditional outputs of the algorithm are the following:
 
 The conditional outputs of the algorithm are the following:
 
+  APosterioriCorrelations
+    *List of matrices*. Each element is an *a posteriori* error correlation
+    matrix of the optimal state.
+
+    Example : ``C = ADD.get("APosterioriCorrelations")[-1]``
+
   APosterioriCovariance
     *List of matrices*. Each element is an *a posteriori* error covariance
     matrix :math:`\mathbf{A}*` of the optimal state.
 
     Example : ``A = ADD.get("APosterioriCovariance")[-1]``
 
+  APosterioriStandardDeviations
+    *List of matrices*. Each element is an *a posteriori* error standard
+    deviation matrix of the optimal state.
+
+    Example : ``E = ADD.get("APosterioriStandardDeviations")[-1]``
+
+  APosterioriVariances
+    *List of matrices*. Each element is an *a posteriori* error variance matrix
+    of the optimal state.
+
+    Example : ``V = ADD.get("APosterioriVariances")[-1]``
+
   BMA
     *List of vectors*. Each element is a vector of difference between the
     background and the optimal state.
@@ -300,6 +315,24 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``so2 = ADD.get("SigmaObs")[-1]``
 
+  SimulatedObservationAtBackground
+    *List of vectors*. Each element is a vector of observation simulated from
+    the background :math:`\mathbf{x}^b`.
+
+    Example : ``hxb = ADD.get("SimulatedObservationAtBackground")[-1]``
+
+  SimulatedObservationAtCurrentState
+    *List of vectors*. Each element is an observed vector at the current state,
+    that is, in the observation space.
+
+    Example : ``Ys = ADD.get("SimulatedObservationAtCurrentState")[-1]``
+
+  SimulatedObservationAtOptimum
+    *List of vectors*. Each element is a vector of observation simulated from
+    the analysis or optimal state :math:`\mathbf{x}^a`.
+
+    Example : ``hxa = ADD.get("SimulatedObservationAtOptimum")[-1]``
+
   SimulationQuantiles
     *List of vectors*. Each element is a vector corresponding to the observed
     state which realize the required quantile, in the same order than the